Overview

Dataset statistics

Number of variables15
Number of observations698
Missing cells0
Missing cells (%)0.0%
Duplicate rows13
Duplicate rows (%)1.9%
Total size in memory77.7 KiB
Average record size in memory114.0 B

Variable types

Boolean2
Categorical5
Numeric8

Alerts

Transported has constant value ""Constant
Dataset has 13 (1.9%) duplicate rowsDuplicates
Cabin_deck is highly overall correlated with HomePlanetHigh correlation
Consumption_Basic is highly overall correlated with FoodCourtHigh correlation
Consumption_High_End is highly overall correlated with HomePlanetHigh correlation
FoodCourt is highly overall correlated with Consumption_BasicHigh correlation
HomePlanet is highly overall correlated with Cabin_deck and 1 other fieldsHigh correlation
CryoSleep is highly imbalanced (75.1%)Imbalance
VIP is highly imbalanced (83.5%)Imbalance
RoomService has 278 (39.8%) zerosZeros
FoodCourt has 305 (43.7%) zerosZeros
ShoppingMall has 322 (46.1%) zerosZeros
Spa has 290 (41.5%) zerosZeros
VRDeck has 312 (44.7%) zerosZeros
Consumption_High_End has 74 (10.6%) zerosZeros
Consumption_Basic has 133 (19.1%) zerosZeros

Reproduction

Analysis started2024-05-07 11:30:16.707818
Analysis finished2024-05-07 11:30:30.099885
Duration13.39 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

CryoSleep
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
False
669 
True
 
29
ValueCountFrequency (%)
False 669
95.8%
True 29
 
4.2%
2024-05-07T13:30:30.263777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Destination
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
TRAPPIST-1e
535 
55 Cancri e
104 
PSO J318.5-22
59 

Length

Max length13
Median length11
Mean length11.169054
Min length11

Characters and Unicode

Total characters7796
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTRAPPIST-1e
2nd rowTRAPPIST-1e
3rd rowPSO J318.5-22
4th rowTRAPPIST-1e
5th rowTRAPPIST-1e

Common Values

ValueCountFrequency (%)
TRAPPIST-1e 535
76.6%
55 Cancri e 104
 
14.9%
PSO J318.5-22 59
 
8.5%

Length

2024-05-07T13:30:30.480855image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:30:30.644031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
trappist-1e 535
55.4%
55 104
 
10.8%
cancri 104
 
10.8%
e 104
 
10.8%
pso 59
 
6.1%
j318.5-22 59
 
6.1%

Most occurring characters

ValueCountFrequency (%)
P 1129
14.5%
T 1070
13.7%
e 639
8.2%
S 594
7.6%
- 594
7.6%
1 594
7.6%
R 535
6.9%
A 535
6.9%
I 535
6.9%
5 267
 
3.4%
Other values (13) 1304
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7796
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 1129
14.5%
T 1070
13.7%
e 639
8.2%
S 594
7.6%
- 594
7.6%
1 594
7.6%
R 535
6.9%
A 535
6.9%
I 535
6.9%
5 267
 
3.4%
Other values (13) 1304
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7796
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 1129
14.5%
T 1070
13.7%
e 639
8.2%
S 594
7.6%
- 594
7.6%
1 594
7.6%
R 535
6.9%
A 535
6.9%
I 535
6.9%
5 267
 
3.4%
Other values (13) 1304
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7796
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 1129
14.5%
T 1070
13.7%
e 639
8.2%
S 594
7.6%
- 594
7.6%
1 594
7.6%
R 535
6.9%
A 535
6.9%
I 535
6.9%
5 267
 
3.4%
Other values (13) 1304
16.7%

VIP
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
False
681 
True
 
17
ValueCountFrequency (%)
False 681
97.6%
True 17
 
2.4%
2024-05-07T13:30:30.827108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

RoomService
Real number (ℝ)

ZEROS 

Distinct334
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean325.44679
Minimum0
Maximum3992
Zeros278
Zeros (%)39.8%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:31.303345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36.5
Q3472
95-th percentile1413.6
Maximum3992
Range3992
Interquartile range (IQR)472

Descriptive statistics

Standard deviation563.64616
Coefficient of variation (CV)1.731915
Kurtosis9.5328805
Mean325.44679
Median Absolute Deviation (MAD)36.5
Skewness2.7549118
Sum227161.86
Variance317697
MonotonicityNot monotonic
2024-05-07T13:30:31.568199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 278
39.8%
1 14
 
2.0%
3 8
 
1.1%
2 6
 
0.9%
8 6
 
0.9%
14 5
 
0.7%
37 3
 
0.4%
5 3
 
0.4%
520 3
 
0.4%
21 3
 
0.4%
Other values (324) 369
52.9%
ValueCountFrequency (%)
0 278
39.8%
1 14
 
2.0%
2 6
 
0.9%
3 8
 
1.1%
4 1
 
0.1%
5 3
 
0.4%
6 1
 
0.1%
7 1
 
0.1%
8 6
 
0.9%
9 2
 
0.3%
ValueCountFrequency (%)
3992 1
0.1%
3677 1
0.1%
3551 1
0.1%
3215 1
0.1%
2997 1
0.1%
2973 1
0.1%
2627 1
0.1%
2532 1
0.1%
2400 1
0.1%
2318 1
0.1%

FoodCourt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct291
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean436.67347
Minimum0
Maximum12809
Zeros305
Zeros (%)43.7%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:31.871484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3415.75
95-th percentile1950.15
Maximum12809
Range12809
Interquartile range (IQR)415.75

Descriptive statistics

Standard deviation1180.4474
Coefficient of variation (CV)2.7032725
Kurtosis45.001873
Mean436.67347
Median Absolute Deviation (MAD)5
Skewness5.901972
Sum304798.08
Variance1393456
MonotonicityNot monotonic
2024-05-07T13:30:32.166350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 305
43.7%
1 18
 
2.6%
2 9
 
1.3%
4 7
 
1.0%
3 6
 
0.9%
5 6
 
0.9%
11 5
 
0.7%
7 5
 
0.7%
9 4
 
0.6%
8 4
 
0.6%
Other values (281) 329
47.1%
ValueCountFrequency (%)
0 305
43.7%
1 18
 
2.6%
2 9
 
1.3%
3 6
 
0.9%
4 7
 
1.0%
5 6
 
0.9%
6 4
 
0.6%
7 5
 
0.7%
8 4
 
0.6%
9 4
 
0.6%
ValueCountFrequency (%)
12809 1
0.1%
11418 1
0.1%
10153 1
0.1%
9495 1
0.1%
8882 1
0.1%
7167 1
0.1%
5600 1
0.1%
4655 1
0.1%
4579 1
0.1%
4545 1
0.1%

ShoppingMall
Real number (ℝ)

ZEROS 

Distinct273
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean216.69666
Minimum0
Maximum2820
Zeros322
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:32.480233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3287.5
95-th percentile1004.8
Maximum2820
Range2820
Interquartile range (IQR)287.5

Descriptive statistics

Standard deviation419.89901
Coefficient of variation (CV)1.9377272
Kurtosis8.8611756
Mean216.69666
Median Absolute Deviation (MAD)2
Skewness2.7476623
Sum151254.27
Variance176315.18
MonotonicityNot monotonic
2024-05-07T13:30:32.739101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 322
46.1%
1 22
 
3.2%
2 11
 
1.6%
3 7
 
1.0%
7 7
 
1.0%
31 6
 
0.9%
13 5
 
0.7%
4 5
 
0.7%
9 4
 
0.6%
5 4
 
0.6%
Other values (263) 305
43.7%
ValueCountFrequency (%)
0 322
46.1%
1 22
 
3.2%
2 11
 
1.6%
3 7
 
1.0%
4 5
 
0.7%
5 4
 
0.6%
6 4
 
0.6%
7 7
 
1.0%
8 2
 
0.3%
9 4
 
0.6%
ValueCountFrequency (%)
2820 1
0.1%
2624 1
0.1%
2387 1
0.1%
2316 1
0.1%
2280 1
0.1%
2155 1
0.1%
2010 1
0.1%
1905 1
0.1%
1865 2
0.3%
1777 1
0.1%

Spa
Real number (ℝ)

ZEROS 

Distinct297
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean259.45296
Minimum0
Maximum4103
Zeros290
Zeros (%)41.5%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:33.052274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q3252.75
95-th percentile1321.15
Maximum4103
Range4103
Interquartile range (IQR)252.75

Descriptive statistics

Standard deviation534.53241
Coefficient of variation (CV)2.0602286
Kurtosis13.465876
Mean259.45296
Median Absolute Deviation (MAD)7
Skewness3.3326597
Sum181098.17
Variance285724.9
MonotonicityNot monotonic
2024-05-07T13:30:33.314837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 290
41.5%
2 13
 
1.9%
1 11
 
1.6%
5 9
 
1.3%
4 7
 
1.0%
3 7
 
1.0%
7 7
 
1.0%
6 5
 
0.7%
15 4
 
0.6%
35 4
 
0.6%
Other values (287) 341
48.9%
ValueCountFrequency (%)
0 290
41.5%
1 11
 
1.6%
2 13
 
1.9%
3 7
 
1.0%
4 7
 
1.0%
4.560684576 1
 
0.1%
5 9
 
1.3%
5.642490463 1
 
0.1%
6 5
 
0.7%
7 7
 
1.0%
ValueCountFrequency (%)
4103 1
0.1%
3733 1
0.1%
3335.657761 1
0.1%
3200 1
0.1%
2868 1
0.1%
2835 1
0.1%
2740 1
0.1%
2627.960902 1
0.1%
2462 1
0.1%
2456 1
0.1%

VRDeck
Real number (ℝ)

ZEROS 

Distinct288
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean254.77527
Minimum0
Maximum5063
Zeros312
Zeros (%)44.7%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:33.561472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q3310.25
95-th percentile1229.45
Maximum5063
Range5063
Interquartile range (IQR)310.25

Descriptive statistics

Standard deviation526.51311
Coefficient of variation (CV)2.0665785
Kurtosis19.587653
Mean254.77527
Median Absolute Deviation (MAD)5
Skewness3.7197499
Sum177833.14
Variance277216.05
MonotonicityNot monotonic
2024-05-07T13:30:33.842755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 312
44.7%
1 15
 
2.1%
2 8
 
1.1%
4 8
 
1.1%
5 5
 
0.7%
6 5
 
0.7%
3 5
 
0.7%
8 4
 
0.6%
33 4
 
0.6%
9 4
 
0.6%
Other values (278) 328
47.0%
ValueCountFrequency (%)
0 312
44.7%
1 15
 
2.1%
2 8
 
1.1%
3 5
 
0.7%
4 8
 
1.1%
5 5
 
0.7%
6 5
 
0.7%
7 1
 
0.1%
8 4
 
0.6%
9 4
 
0.6%
ValueCountFrequency (%)
5063 1
0.1%
3880 1
0.1%
3460 1
0.1%
3186 1
0.1%
3139 1
0.1%
2630 1
0.1%
2577 1
0.1%
2393.9427 1
0.1%
2376 1
0.1%
2260 1
0.1%

Cabin_deck
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
F
294 
G
171 
E
108 
D
45 
C
43 
Other values (2)
37 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters698
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowF
4th rowF
5th rowE

Common Values

ValueCountFrequency (%)
F 294
42.1%
G 171
24.5%
E 108
 
15.5%
D 45
 
6.4%
C 43
 
6.2%
B 19
 
2.7%
A 18
 
2.6%

Length

2024-05-07T13:30:34.076275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:30:34.294221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 294
42.1%
g 171
24.5%
e 108
 
15.5%
d 45
 
6.4%
c 43
 
6.2%
b 19
 
2.7%
a 18
 
2.6%

Most occurring characters

ValueCountFrequency (%)
F 294
42.1%
G 171
24.5%
E 108
 
15.5%
D 45
 
6.4%
C 43
 
6.2%
B 19
 
2.7%
A 18
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 294
42.1%
G 171
24.5%
E 108
 
15.5%
D 45
 
6.4%
C 43
 
6.2%
B 19
 
2.7%
A 18
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 294
42.1%
G 171
24.5%
E 108
 
15.5%
D 45
 
6.4%
C 43
 
6.2%
B 19
 
2.7%
A 18
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 294
42.1%
G 171
24.5%
E 108
 
15.5%
D 45
 
6.4%
C 43
 
6.2%
B 19
 
2.7%
A 18
 
2.6%

Group_size
Real number (ℝ)

Distinct8
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9813754
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:34.473960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7590748
Coefficient of variation (CV)0.88780493
Kurtosis2.9192566
Mean1.9813754
Median Absolute Deviation (MAD)0
Skewness1.9808237
Sum1383
Variance3.0943442
MonotonicityNot monotonic
2024-05-07T13:30:34.677443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 445
63.8%
2 106
 
15.2%
3 51
 
7.3%
7 30
 
4.3%
5 27
 
3.9%
6 16
 
2.3%
4 13
 
1.9%
8 10
 
1.4%
ValueCountFrequency (%)
1 445
63.8%
2 106
 
15.2%
3 51
 
7.3%
4 13
 
1.9%
5 27
 
3.9%
6 16
 
2.3%
7 30
 
4.3%
8 10
 
1.4%
ValueCountFrequency (%)
8 10
 
1.4%
7 30
 
4.3%
6 16
 
2.3%
5 27
 
3.9%
4 13
 
1.9%
3 51
 
7.3%
2 106
 
15.2%
1 445
63.8%

HomePlanet
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Earth
468 
Mars
134 
Europa
96 

Length

Max length6
Median length5
Mean length4.9455587
Min length4

Characters and Unicode

Total characters3452
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEarth
2nd rowEarth
3rd rowEarth
4th rowEarth
5th rowEarth

Common Values

ValueCountFrequency (%)
Earth 468
67.0%
Mars 134
 
19.2%
Europa 96
 
13.8%

Length

2024-05-07T13:30:34.923324image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:30:35.157739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
earth 468
67.0%
mars 134
 
19.2%
europa 96
 
13.8%

Most occurring characters

ValueCountFrequency (%)
a 698
20.2%
r 698
20.2%
E 564
16.3%
t 468
13.6%
h 468
13.6%
M 134
 
3.9%
s 134
 
3.9%
u 96
 
2.8%
o 96
 
2.8%
p 96
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 698
20.2%
r 698
20.2%
E 564
16.3%
t 468
13.6%
h 468
13.6%
M 134
 
3.9%
s 134
 
3.9%
u 96
 
2.8%
o 96
 
2.8%
p 96
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 698
20.2%
r 698
20.2%
E 564
16.3%
t 468
13.6%
h 468
13.6%
M 134
 
3.9%
s 134
 
3.9%
u 96
 
2.8%
o 96
 
2.8%
p 96
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 698
20.2%
r 698
20.2%
E 564
16.3%
t 468
13.6%
h 468
13.6%
M 134
 
3.9%
s 134
 
3.9%
u 96
 
2.8%
o 96
 
2.8%
p 96
 
2.8%

Transported
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
1
698 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters698
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 698
100.0%

Length

2024-05-07T13:30:35.377942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:30:35.561851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 698
100.0%

Most occurring characters

ValueCountFrequency (%)
1 698
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 698
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 698
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 698
100.0%

Consumption_High_End
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct537
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean839.67502
Minimum0
Maximum5502.6578
Zeros74
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:35.748711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1247.25
median651.5
Q31044
95-th percentile2547.7
Maximum5502.6578
Range5502.6578
Interquartile range (IQR)796.75

Descriptive statistics

Standard deviation859.81638
Coefficient of variation (CV)1.0239871
Kurtosis5.2345311
Mean839.67502
Median Absolute Deviation (MAD)402.5
Skewness2.0453295
Sum586093.16
Variance739284.21
MonotonicityNot monotonic
2024-05-07T13:30:35.985869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
10.6%
801 4
 
0.6%
697 3
 
0.4%
409 3
 
0.4%
921 3
 
0.4%
803 3
 
0.4%
249 3
 
0.4%
264 3
 
0.4%
851 2
 
0.3%
707 2
 
0.3%
Other values (527) 598
85.7%
ValueCountFrequency (%)
0 74
10.6%
13 1
 
0.1%
21 1
 
0.1%
43 1
 
0.1%
55 1
 
0.1%
57 1
 
0.1%
61 1
 
0.1%
62 1
 
0.1%
68 1
 
0.1%
71 1
 
0.1%
ValueCountFrequency (%)
5502.657761 1
0.1%
5141 1
0.1%
4851 1
0.1%
4528 1
0.1%
4522 1
0.1%
4349 1
0.1%
4103 1
0.1%
3994 1
0.1%
3901 1
0.1%
3880 1
0.1%

Consumption_Basic
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct447
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean653.37012
Minimum0
Maximum12811
Zeros133
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size10.9 KiB
2024-05-07T13:30:36.199483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.25
median343.5
Q3752.75
95-th percentile2402
Maximum12811
Range12811
Interquartile range (IQR)746.5

Descriptive statistics

Standard deviation1209.8164
Coefficient of variation (CV)1.8516555
Kurtosis37.365571
Mean653.37012
Median Absolute Deviation (MAD)341
Skewness5.1942214
Sum456052.35
Variance1463655.7
MonotonicityNot monotonic
2024-05-07T13:30:36.490242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 133
 
19.1%
1 15
 
2.1%
5 9
 
1.3%
6 6
 
0.9%
7 5
 
0.7%
3 5
 
0.7%
18 4
 
0.6%
9 4
 
0.6%
33 4
 
0.6%
31 4
 
0.6%
Other values (437) 509
72.9%
ValueCountFrequency (%)
0 133
19.1%
1 15
 
2.1%
2 4
 
0.6%
3 5
 
0.7%
4 3
 
0.4%
5 9
 
1.3%
6 6
 
0.9%
7 5
 
0.7%
8 2
 
0.3%
9 4
 
0.6%
ValueCountFrequency (%)
12811 1
0.1%
11418 1
0.1%
10154 1
0.1%
9495 1
0.1%
8903 1
0.1%
7167 1
0.1%
6315 1
0.1%
4816 1
0.1%
4790 1
0.1%
4580 1
0.1%

Age_group
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Young adults
446 
Middle-aged
157 
Minor
80 
Senior
 
15

Length

Max length12
Median length12
Mean length10.84384
Min length5

Characters and Unicode

Total characters7569
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYoung adults
2nd rowMinor
3rd rowMiddle-aged
4th rowYoung adults
5th rowMinor

Common Values

ValueCountFrequency (%)
Young adults 446
63.9%
Middle-aged 157
 
22.5%
Minor 80
 
11.5%
Senior 15
 
2.1%

Length

2024-05-07T13:30:36.749317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-07T13:30:36.980350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
young 446
39.0%
adults 446
39.0%
middle-aged 157
 
13.7%
minor 80
 
7.0%
senior 15
 
1.3%

Most occurring characters

ValueCountFrequency (%)
d 917
12.1%
u 892
11.8%
l 603
 
8.0%
g 603
 
8.0%
a 603
 
8.0%
n 541
 
7.1%
o 541
 
7.1%
t 446
 
5.9%
s 446
 
5.9%
Y 446
 
5.9%
Other values (7) 1531
20.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 917
12.1%
u 892
11.8%
l 603
 
8.0%
g 603
 
8.0%
a 603
 
8.0%
n 541
 
7.1%
o 541
 
7.1%
t 446
 
5.9%
s 446
 
5.9%
Y 446
 
5.9%
Other values (7) 1531
20.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 917
12.1%
u 892
11.8%
l 603
 
8.0%
g 603
 
8.0%
a 603
 
8.0%
n 541
 
7.1%
o 541
 
7.1%
t 446
 
5.9%
s 446
 
5.9%
Y 446
 
5.9%
Other values (7) 1531
20.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 917
12.1%
u 892
11.8%
l 603
 
8.0%
g 603
 
8.0%
a 603
 
8.0%
n 541
 
7.1%
o 541
 
7.1%
t 446
 
5.9%
s 446
 
5.9%
Y 446
 
5.9%
Other values (7) 1531
20.2%

Interactions

2024-05-07T13:30:28.014095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:17.582271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:19.329497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.757433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.273268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.601296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.909916image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:26.532409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:28.238017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:17.812562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:19.518005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.934991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.431814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.778691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:25.130977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:26.742795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:28.400996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:18.074314image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:19.680842image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:21.164142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.588267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.949742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:25.342111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:26.946870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:28.565364image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:18.276036image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:19.860312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:21.320024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.786724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.118793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:25.551712image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:27.138421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:28.726505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:18.465492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.037391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:21.510836image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.959038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.267717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:25.770305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:27.317938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:28.901662image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:18.699561image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.255766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:21.688426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.114459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.446443image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:25.948656image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:27.507128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:29.105669image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:18.951069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.418195image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:21.865065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.279220image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.613629image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:26.143611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:27.683021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:29.249659image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:19.149040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:20.583704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:22.051262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:23.451810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:24.766450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:26.308753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-07T13:30:27.859588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-05-07T13:30:37.145519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Age_groupCabin_deckConsumption_BasicConsumption_High_EndCryoSleepDestinationFoodCourtGroup_sizeHomePlanetRoomServiceShoppingMallSpaVIPVRDeck
Age_group1.0000.179-0.0060.0500.1260.042-0.013-0.1270.1450.0710.0460.0220.1200.048
Cabin_deck0.1791.000-0.427-0.4360.2540.183-0.331-0.0530.739-0.036-0.070-0.2760.236-0.225
Consumption_Basic-0.006-0.4271.0000.1780.0000.1020.640-0.0270.4290.0500.4790.2490.2540.167
Consumption_High_End0.050-0.4360.1781.0000.2150.1250.242-0.0390.5060.344-0.0330.4360.2320.406
CryoSleep0.1260.2540.0000.2151.0000.102-0.2120.1980.171-0.224-0.205-0.2190.000-0.209
Destination0.0420.1830.1020.1250.1021.000-0.1100.0470.168-0.0200.047-0.0980.000-0.111
FoodCourt-0.013-0.3310.6400.242-0.212-0.1101.000-0.0350.465-0.126-0.1620.2920.2770.285
Group_size-0.127-0.053-0.027-0.0390.1980.047-0.0351.0000.214-0.147-0.102-0.0490.093-0.058
HomePlanet0.1450.7390.4290.5060.1710.1680.4650.2141.0000.2340.2380.0430.239-0.051
RoomService0.071-0.0360.0500.344-0.224-0.020-0.126-0.1470.2341.0000.262-0.1500.000-0.258
ShoppingMall0.046-0.0700.479-0.033-0.2050.047-0.162-0.1020.2380.2621.0000.0100.069-0.105
Spa0.022-0.2760.2490.436-0.219-0.0980.292-0.0490.043-0.1500.0101.0000.1960.123
VIP0.1200.2360.2540.2320.0000.0000.2770.0930.2390.0000.0690.1961.0000.052
VRDeck0.048-0.2250.1670.406-0.209-0.1110.285-0.058-0.051-0.258-0.1050.1230.0521.000

Missing values

2024-05-07T13:30:29.504030image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-07T13:30:29.882691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
1FalseTRAPPIST-1eFalse109.0000009.025.0549.044.0F1Earth1702.00000034.0Young adults
4FalseTRAPPIST-1eFalse303.00000070.0151.0565.02.0F1Earth1870.000000221.0Minor
5FalsePSO J318.5-22False0.000000483.00.0291.00.0F1Earth1291.000000483.0Middle-aged
8FalseTRAPPIST-1eFalse0.000000785.017.0216.00.0F1Earth1216.000000802.0Young adults
24FalseTRAPPIST-1eFalse0.0000000.00.00.00.0E6Earth10.0000000.0Minor
33FalseTRAPPIST-1eFalse214.0000000.01411.00.01229.0F3Mars11443.0000001411.0Middle-aged
35FalseTRAPPIST-1eFalse829.8530540.01750.0990.00.0F3Mars11819.8530541750.0Young adults
65FalseTRAPPIST-1eFalse887.0000000.09.06.00.0F1Earth1893.0000009.0Middle-aged
78FalseTRAPPIST-1eFalse688.0000000.00.00.017.0G1Earth1705.0000000.0Young adults
105FalseTRAPPIST-1eFalse2209.00000011418.00.01868.0445.0B4Europa14522.00000011418.0Middle-aged
CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group
8569FalseTRAPPIST-1eFalse270.04.0453.0000007.00.0F1Earth1277.0457.000000Young adults
8572FalseTRAPPIST-1eFalse698.094.00.0000000.00.0E1Earth1698.094.000000Middle-aged
8576FalseTRAPPIST-1eFalse802.00.01.0000001.00.0F1Earth1803.01.000000Young adults
8600FalseTRAPPIST-1eFalse0.090.00.000000795.00.0F1Earth1795.090.000000Young adults
8604FalseTRAPPIST-1eFalse6.00.00.0000000.01164.0F2Earth11170.00.000000Middle-aged
8607FalsePSO J318.5-22False623.00.0111.8544450.053.0F3Earth1676.0111.854445Young adults
8618FalseTRAPPIST-1eTrue0.05600.0715.0000002868.0971.0C1Europa13839.06315.000000Young adults
8624FalseTRAPPIST-1eFalse8.0752.00.0000000.0687.0F1Earth1695.0752.000000Young adults
8641FalseTRAPPIST-1eFalse1030.01015.00.00000011.00.0F1Earth11041.01015.000000Middle-aged
8648FalseTRAPPIST-1eFalse240.0242.0510.0000000.00.0G2Earth1240.0752.000000Young adults

Duplicate rows

Most frequently occurring

CryoSleepDestinationVIPRoomServiceFoodCourtShoppingMallSpaVRDeckCabin_deckGroup_sizeHomePlanetTransportedConsumption_High_EndConsumption_BasicAge_group# duplicates
5FalseTRAPPIST-1eFalse0.00.00.00.00.0G5Earth10.00.0Minor9
8TrueTRAPPIST-1eFalse0.00.00.00.00.0E1Mars10.00.0Young adults9
11TrueTRAPPIST-1eFalse0.00.00.00.00.0G7Earth10.00.0Minor8
6FalseTRAPPIST-1eFalse0.00.00.00.00.0G6Earth10.00.0Minor7
7FalseTRAPPIST-1eFalse0.00.00.00.00.0G7Earth10.00.0Minor7
2FalseTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Minor5
3FalseTRAPPIST-1eFalse0.00.00.00.00.0G1Earth10.00.0Young adults4
9TrueTRAPPIST-1eFalse0.00.00.00.00.0E2Mars10.00.0Young adults3
0FalseTRAPPIST-1eFalse0.00.00.00.00.0E1Mars10.00.0Young adults2
1FalseTRAPPIST-1eFalse0.00.00.00.00.0E6Earth10.00.0Minor2